This disclosure is directed to optimizing the interactions between a virtual machine (“VM” or “VM client”) executing on a physical computer, and a storage array storing data and receiving requests from the VM. In particular, this disclosure is directed to providing input/output (“I/O”) scheduling capabilities to a VM that interacts with a storage array. This disclosure is also directed to providing I/O scheduling capabilities to any physical hardware capable of hosting a VM.
On a conventional computing platform, I/O scheduling (also known as “disk scheduling”) is an operating system process for organizing read/write requests, commands and/or operations submitted to a storage volume. This process may be performed by an I/O scheduler software application. An I/O scheduler may be configured to reduce “seek time,” or the length of time it takes to access data on a storage volume. In some cases, the location of data on the storage volume can affect seek time. For example, if the storage volume is a local hard drive, an I/O scheduler can reduce seek time by intelligently submitting requests in such a way as to minimize drive head movement.
An I/O scheduler may also be configured to reduce “latency,” or the delay in time between the submission of a request to a storage volume and receipt of the request at the storage system. Requests may be submitted to a storage volume in the form of a queue. If the queue is not managed efficiently, there may an overabundance of requests submitted to the storage volume, causing network traffic and reducing overall bandwidth. To minimize latency, an I/O scheduler may adopt various queue control methods.
An I/O scheduler may use one or more scheduling algorithms to minimize seek time, reduce latency, or otherwise streamline the request submission process. Scheduling algorithms may use data from the computing platform's operating system, data from applications running on the operating system, and/or data pertaining to the storage volume. However, the data available for current scheduling algorithms and I/O schedulers is only useful in accessing local hard drives. As a result, current I/O schedulers are ineffective in an environment where read/write requests are submitted to a remote storage array, such as that found in a cloud computing network. In other words, even though a VM may have I/O scheduling software as part of its operating system, the data used by this software is directed to optimizing local storage, not cloud-based storage.
Moreover, running a current I/O scheduler on a VM can also be ineffective, since current I/O schedulers are not configured for operation in a virtual environment where multiple VMs can run on a single computing platform, or VM host server. A current I/O scheduler only contemplates managing requests from a single client. As more VMs are launched using a single computing platform, a current I/O scheduler is unable to organize requests from multiple VM clients, which causes network traffic, bandwidth issues, and ultimately, a loss in productivity from the VMs whose requests are not being processed.
Previous attempts to provide I/O scheduling functions to VMs only manage bandwidth between a server hosting the VMs and an associated storage volume. For example, storage I/O control (“SIOC”), such as that provided for the VMware® vSphere™, prioritizes a VM's access to storage volume shares based upon the number of shares allocated to that VM. In other words, SIOC provides I/O control to the storage volume, but not directly on or from the VM. SIOC monitors network traffic from multiple VM servers to a single remote storage volume (or “datastore”). Each VM server can host one or more VMs, and each VM executing on the VM server has an allocated number of shares on the storage volume. In addition, each VM transmit a single queue of requests to the storage volume. When latency for a queue exceeds a certain threshold (e.g., 30 ms), the bandwidth is reduced in proportion to the number of storage volume shares allocated to each VM. A VM with more shares on the storage volume will be allowed a larger percentage of available bandwidth, and a VM will less shares on the storage volume will be allowed a smaller percentage of overall bandwidth. While such I/O control measures may help control overall network traffic, this type of queue management is not sufficiently granular for individual requests submitted from one VM to a remote storage array, nor do these measures consider the priority of individual requests from each VM.